We have been developing tools and resources that make it possible to analyze a large number of genes in various experimental conditions. In our earlier work, we 1) constructed cDNA libraries from early mouse embryos and stem cells and generated a large number of expressed sequence tags (ESTs), 2) developed a glass-slide microarray platform containing in situ-synthesized 60-mer oligonucleotide probes representing approximately 44,000 unique mouse transcripts, 3) produced the web-based ANOVA-FDR software to provide user-friendly microarray data analysis, and 4) developed an algorithm and a fully-automated computational pipeline for transcript assembly from expressed sequences aligned to the mouse genome. In addition, we recently developed a comprehensive database and web browser of the binding sites of transcription factors (TFs) and cis-regulatory modules (CRMs) on the mouse genome. These resources and tools are now applied to the systematic analysis of gene regulatory networks in mouse embryonic stem cells. In our pilot project, we have demonstrated that it is possible to analyze and identify downstream target genes by monitoring the global gene expression patterns of mouse ES cell lines, when a gene encoding a specific TF (Pou5f1 or Oct4 in this case) is manipulated so that the gene can be overexpressed or repressed. To extend our strategy further, we generated 137 ES cell lines thus far, in each of which one of a total of 137 different TFs can be overexpressed in a tetracycline-inducible manner. We have been characterizing these ES cell lines as follows: (i) subcellular localization of Flag-tagged transcription factors by immunohistochemistry; (ii) induction levels of the manipulated transcription factors by quantitative RT-PCR, (iii) DNA microarray-based expression profiling before and after the induction of transcription factors; (iv) western blotting, and (v) karyotyping. Together, these results indicate that we have generated reliable TF-manipulable ES cell lines. We carried out detailed analyses of the first 50 ES cell lines and found that among the 50 TFs, Cdx2 provoked the most extensive transcriptome perturbation in ES cells, followed by Esx1, Sox9, Tcf3, Klf4, and Gata3. ChIP-Seq revealed that CDX2 binds to promoters of up-regulated target genes. By contrast, genes down-regulated by CDX2 did not show CDX2 binding, but were enriched with binding sites for POU5F1, SOX2, and NANOG. Genes with binding sites for these core TFs were also down-regulated by the induction of at least 15 other TFs, suggesting a common initial step for ES cell differentiation mediated by interference with the binding of core TFs to their target genes. Further analyses of additional TF-manipulable mouse ES cell lines demonstrated that indeed overexpression of a single TF is sufficient to initiate the differentiation of mouse ES cells into specific cell lineages. In general, it has been thought that loss-of-function studies are more useful for delineating a gene network and revealing the function of a gene than the gain-of-function studies. Therefore, in addition to the overexpression of TFs (i.e., gain-of-function study), we systematically repressed each of 100 TFs with shRNA and carried out global gene expression profiling in mouse embryonic stem (ES) cells. Unexpectedly, only the repression of a handful of TFs significantly affected transcriptomes, which changed in two directions/trajectories: one trajectory by the repression of either Pou5f1 or Sox2; the other trajectory by the repression of either Esrrb, Sall4, Nanog, or Tcfap4. The data suggest that the trajectories of gene expression change are already preconfigured by the gene regulatory network and roughly correspond to extraembryonic and embryonic fates of cell differentiation, respectively. These data also indicate the robustness of the pluripotency gene network, as the transient repression of most TFs did not alter the transcriptomes. One of our initial goals was to identify TFs that can induce differentiation of mouse ES cells into specific cell types. To this end, we compared the global gene expression profiles generated after the induction of a specific TF to the global gene expression profiles of various tissue and cell types. The analyses predicted the direction of differentiation by the overexpression of a specific TF. For example, the overexpression of TFs - Myod1, Mef2c, Esx1, Foxa1, Hnf4a, Gata2, Gata3, Myc, Elf5, Irf2, Elf1, Sfpi1, Ets1, Smad7, Nr2f1, Sox11, Dmrt1, Sox9, Foxg1, Sox2, or Ascl1 - can direct efficient, specific, and rapid differentiation into myocytes, hepatocytes, blood cells, and neurons. These results suggest the utility of this approach to identify a TF that can be used to induce mouse ES cell differentiation.